{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print 'Training data shape: ', X_train.shape\n",
    "print 'Training labels shape: ', y_train.shape\n",
    "print 'Test data shape: ', X_test.shape\n",
    "print 'Test labels shape: ', y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Crc0+AKeOH+HAEeU3/+yv/7fb0vgD/+JVN09FvIJblK8Bn/yZ4rtGBLH96lIrO+Tzvbhp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SgkTEcHgQefixKkCCA5OawHNdRatAlLF8gS+l3X0zBusTmFiFnfoLRBwHNIqgT1apE8ePA8Gh/z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GmyBGmaIGe4IohCU0GfZBm0oF3h8Vjhv/vv/wwA8HD3DL/y5hsAgIa7AdtjsbG2h5Jf3HoA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\n0bD49Fnqq7cebKK3S0LKansJnkekfqvLEkmfsozuj4ewDRI2pM6Rs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jed6HJm38sOdPr5kqpbPtqkLZH47m+ygssDndh3JKFKREr0p3oM10Q7ASmrOR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\nRERERBMwiCIiIiKa4D+gRPxR2CuKIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f21f1c4bdd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print 'Train data shape: ', X_train.shape\n",
    "print 'Train labels shape: ', y_train.shape\n",
    "print 'Validation data shape: ', X_val.shape\n",
    "print 'Validation labels shape: ', y_val.shape\n",
    "print 'Test data shape: ', X_test.shape\n",
    "print 'Test labels shape: ', y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print 'Training data shape: ', X_train.shape\n",
    "print 'Validation data shape: ', X_val.shape\n",
    "print 'Test data shape: ', X_test.shape\n",
    "print 'dev data shape: ', X_dev.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 130.64189796  135.98173469  132.47391837  130.05569388  135.34804082\n",
      "  131.75402041  130.96055102  136.14328571  132.47636735  131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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tzI/nwTbNbAJOfLMKOfHNKuTEN6vQ2MSX9AFJj0t6StKzkv6tWX6GpB2SXpT0gKfJNnv/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\nE9+sQv8PhW6BNuofxXAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2214a9f5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print mean_image[:10] # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print X_train.shape, X_val.shape, X_test.shape, X_dev.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 8.964128\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "print 'loss: %f' % (loss, )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: 9.560674 analytic: 9.560674, relative error: 3.259413e-11\n",
      "numerical: -52.691756 analytic: -52.691756, relative error: 1.070505e-11\n",
      "numerical: 11.585791 analytic: 11.585791, relative error: 3.386676e-11\n",
      "numerical: -8.064957 analytic: -8.064957, relative error: 2.303715e-11\n",
      "numerical: 26.531498 analytic: 26.531498, relative error: 9.319823e-14\n",
      "numerical: 22.725724 analytic: 22.725724, relative error: 2.502922e-12\n",
      "numerical: 13.152851 analytic: 13.152851, relative error: 1.828319e-11\n",
      "numerical: -12.282456 analytic: -12.282456, relative error: 3.118691e-11\n",
      "numerical: -49.049460 analytic: -49.049460, relative error: 4.828412e-12\n",
      "numerical: 4.489666 analytic: 4.489666, relative error: 2.759671e-11\n",
      "numerical: -16.098897 analytic: -16.098897, relative error: 1.919502e-11\n",
      "numerical: -13.811021 analytic: -13.811021, relative error: 3.172865e-11\n",
      "numerical: 1.841892 analytic: 1.841892, relative error: 4.394644e-11\n",
      "numerical: 13.545417 analytic: 13.545417, relative error: 3.890049e-11\n",
      "numerical: 20.107938 analytic: 20.107938, relative error: 5.812489e-12\n",
      "numerical: -11.573286 analytic: -11.573286, relative error: 4.358528e-11\n",
      "numerical: 7.507631 analytic: 7.507631, relative error: 5.837978e-12\n",
      "numerical: 0.135351 analytic: 0.135351, relative error: 2.445013e-09\n",
      "numerical: -8.594299 analytic: -8.594299, relative error: 4.444251e-11\n",
      "numerical: 7.472032 analytic: 7.472032, relative error: 1.821293e-11\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 1e2)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 1e2)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline Question 1:\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "**Your Answer:** *In zero point, the Hinge Loss is not differentiable, so gradient check may fail.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 8.964128e+00 computed in 0.146387s\n",
      "Vectorized loss: 8.964128e+00 computed in 0.009617s\n",
      "difference: -0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'Naive loss: %e computed in %fs' % (loss_naive, toc - tic)\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic)\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print 'difference: %f' % (loss_naive - loss_vectorized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.180570s\n",
      "Vectorized loss and gradient: computed in 0.010150s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'Naive loss and gradient: computed in %fs' % (toc - tic)\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'Vectorized loss and gradient: computed in %fs' % (toc - tic)\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print 'difference: %f' % difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 788.726644\n",
      "iteration 100 / 1500: loss 286.969521\n",
      "iteration 200 / 1500: loss 107.798223\n",
      "iteration 300 / 1500: loss 42.092783\n",
      "iteration 400 / 1500: loss 18.558594\n",
      "iteration 500 / 1500: loss 9.987726\n",
      "iteration 600 / 1500: loss 7.154248\n",
      "iteration 700 / 1500: loss 5.797313\n",
      "iteration 800 / 1500: loss 5.307208\n",
      "iteration 900 / 1500: loss 5.484743\n",
      "iteration 1000 / 1500: loss 5.821926\n",
      "iteration 1100 / 1500: loss 5.972228\n",
      "iteration 1200 / 1500: loss 4.712599\n",
      "iteration 1300 / 1500: loss 5.657978\n",
      "iteration 1400 / 1500: loss 5.227767\n",
      "That took 12.579249s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print 'That took %fs' % (toc - tic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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kSVLt1HTYtFLeyLDpM8/ASSfB6tUVLkqSJGkvKj1s2nThbedO6N8ftmyBvn0rXJgkSdIe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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2214a9f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.369980\n",
      "validation accuracy: 0.386000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print 'training accuracy: %f' % (np.mean(y_train == y_train_pred), )\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print 'validation accuracy: %f' % (np.mean(y_val == y_val_pred), )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 798.267239\n",
      "iteration 100 / 1500: loss 289.568625\n",
      "iteration 200 / 1500: loss 109.130734\n",
      "iteration 300 / 1500: loss 43.077909\n",
      "iteration 400 / 1500: loss 19.501388\n",
      "iteration 500 / 1500: loss 10.586024\n",
      "iteration 600 / 1500: loss 7.151213\n",
      "iteration 700 / 1500: loss 6.184695\n",
      "iteration 800 / 1500: loss 5.616355\n",
      "iteration 900 / 1500: loss 5.841307\n",
      "iteration 1000 / 1500: loss 5.749702\n",
      "iteration 1100 / 1500: loss 5.147611\n",
      "iteration 1200 / 1500: loss 5.293773\n",
      "iteration 1300 / 1500: loss 5.015974\n",
      "iteration 1400 / 1500: loss 5.312522\n",
      "iteration 0 / 1500: loss 1570.851748\n",
      "iteration 100 / 1500: loss 212.889183\n",
      "iteration 200 / 1500: loss 33.621741\n",
      "iteration 300 / 1500: loss 8.993607\n",
      "iteration 400 / 1500: loss 6.142721\n",
      "iteration 500 / 1500: loss 5.357372\n",
      "iteration 600 / 1500: loss 6.277398\n",
      "iteration 700 / 1500: loss 6.017725\n",
      "iteration 800 / 1500: loss 5.691403\n",
      "iteration 900 / 1500: loss 5.923456\n",
      "iteration 1000 / 1500: loss 5.234096\n",
      "iteration 1100 / 1500: loss 5.640846\n",
      "iteration 1200 / 1500: loss 6.017300\n",
      "iteration 1300 / 1500: loss 5.513140\n",
      "iteration 1400 / 1500: loss 5.961878\n",
      "iteration 0 / 1500: loss 802.492298\n",
      "iteration 100 / 1500: loss 19.940247\n",
      "iteration 200 / 1500: loss 24.874224\n",
      "iteration 300 / 1500: loss 19.030112\n",
      "iteration 400 / 1500: loss 19.072536\n",
      "iteration 500 / 1500: loss 26.753457\n",
      "iteration 600 / 1500: loss 20.039701\n",
      "iteration 700 / 1500: loss 13.922277\n",
      "iteration 800 / 1500: loss 25.380372\n",
      "iteration 900 / 1500: loss 27.239189\n",
      "iteration 1000 / 1500: loss 28.336392\n",
      "iteration 1100 / 1500: loss 30.841508\n",
      "iteration 1200 / 1500: loss 19.517046\n",
      "iteration 1300 / 1500: loss 23.036683\n",
      "iteration 1400 / 1500: loss 19.841206\n",
      "iteration 0 / 1500: loss 1569.147799\n",
      "iteration 100 / 1500: loss 30.870277\n",
      "iteration 200 / 1500: loss 27.262774\n",
      "iteration 300 / 1500: loss 28.502652\n",
      "iteration 400 / 1500: loss 27.263902\n",
      "iteration 500 / 1500: loss 31.705941\n",
      "iteration 600 / 1500: loss 22.518295\n",
      "iteration 700 / 1500: loss 33.908980\n",
      "iteration 800 / 1500: loss 41.215109\n",
      "iteration 900 / 1500: loss 25.177062\n",
      "iteration 1000 / 1500: loss 38.780758\n",
      "iteration 1100 / 1500: loss 27.813950\n",
      "iteration 1200 / 1500: loss 21.831928\n",
      "iteration 1300 / 1500: loss 43.927496\n",
      "iteration 1400 / 1500: loss 27.251013\n",
      "lr 1.000000e-07 reg 5.000000e+04 train accuracy: 0.374061 val accuracy: 0.370000\n",
      "lr 1.000000e-07 reg 1.000000e+05 train accuracy: 0.361898 val accuracy: 0.364000\n",
      "lr 5.000000e-06 reg 5.000000e+04 train accuracy: 0.196000 val accuracy: 0.194000\n",
      "lr 5.000000e-06 reg 1.000000e+05 train accuracy: 0.173633 val accuracy: 0.168000\n",
      "best validation accuracy achieved during cross-validation: 0.370000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.4 on the validation set.\n",
    "learning_rates = [1e-7, 5e-6]\n",
    "regularization_strengths = [5e4, 1e5]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "pass\n",
    "for lr in learning_rates:\n",
    "    for rs in regularization_strengths:\n",
    "        svm = LinearSVM()\n",
    "        loss_hist = svm.train(X_train, y_train, learning_rate=lr, reg=rs,\n",
    "                      num_iters=1500, verbose=True)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        train_acc = np.mean(y_train == y_train_pred)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        val_acc = np.mean(y_val == y_val_pred)\n",
    "        \n",
    "        results[(lr, rs)] = (train_acc, val_acc)\n",
    "        \n",
    "        if val_acc > best_val:\n",
    "            best_val = val_acc\n",
    "            best_svm = svm\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print 'lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy)\n",
    "    \n",
    "print 'best validation accuracy achieved during cross-validation: %f' % best_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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KguEsSYMiYnbaPBR4rFz8qtQFJWkC8Hvg28CXgeOAVyNipdvKirT9EXA0sAxY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\n7/8DGBdyr81mLIYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f21edfb2610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.372000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print 'linear SVM on raw pixels final test set accuracy: %f' % test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FMc7BVuAWbeCwrIdozxlc0ZT9EmS1FtsXBrTnbgPZB8ens7EdDSdIam18Vg2J8OQe3LJm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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f21eb75d210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in xrange(10):\n",
    "  plt.subplot(2, 5, i + 1)\n",
    "    \n",
    "  # Rescale the weights to be between 0 and 255\n",
    "  wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "  plt.imshow(wimg.astype('uint8'))\n",
    "  plt.axis('off')\n",
    "  plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 2:\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "**Your answer:** *you can treat svm classifier as template matching*"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
